code: def test_test_selector_with_one_variable(df_test):
      X, y = df_test
  
      sel = SelectBySingleFeaturePerformance(
          RandomForestClassifier(random_state=1),
          variables=["var_0"],
          threshold=0.5,
      )
      sel.fit(X, y)
  
      # expected result
      Xtransformed = X.copy()
  
      assert sel.features_to_drop_ == []
      assert sel.feature_performance_ == {
          "var_0": 0.5957642619540211,
      }
      # test transform output
      pd.testing.assert_frame_equal(sel.transform(X), Xtransformed)
  
      sel = SelectBySingleFeaturePerformance(
          RandomForestClassifier(random_state=1),
          variables=["var_3"],
          threshold=0.5,
      )
      sel.fit(X, y)
  
      # expected result
      Xtransformed = X.copy()
      Xtransformed.drop(columns=["var_3"], inplace=True)
  
      assert sel.features_to_drop_ == ["var_3"]
      assert sel.feature_performance_ == {
          "var_3": 0.4752954458526748,
      }
      # test transform output
      pd.testing.assert_frame_equal(sel.transform(X), Xtransformed)
  
file_path: feature_engine/tests/test_selection/test_single_feature_performance_selection.py
imports: import warnings
  import numpy
  import pandas
  import pytest
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.linear_model import LinearRegression
  from sklearn.tree import DecisionTreeRegressor
  from feature_engine.selection import SelectBySingleFeaturePerformance
label_desc: Use image node_id 10 for calling a global function with example usage: test_test_selector_with_one_variable(df_test) without return types